Conventional approaches to navigating a previously unrecorded space require concurrently solving two related problems: understanding the layout of the space in order to create a map, and, at the same time, locating one's own position on the map. In the fields of robotics and computer vision, this is known as the Simultaneous Localization and Mapping (SLAM) problem. The relevance of this problem to the field of robotics and autonomous systems is self-evident. However, it is also a critical functionality for many other applications in the field of computer vision, especially in the areas of 3D reconstruction and scene understanding. Visual SLAM (VSLAM) solutions rely on input from image sensors, possibly in addition to other sensors, and have certain advantages over other input devices, such as the ability to calculate one's absolute (as opposed to relative) location in the world and support for a high level of precision.